Partner with stakeholders to clarify business questions into ML problem statements (classification, ranking, uplift, forecasting, optimization, GenAI RAG/agentic workflows, etc.).
Write and maintain an ML System Design Spec: problem hypothesis, decision loop, users, constraints, acceptable risk, SLAs/SLOs, and post-deployment guardrails.
Conduct advanced exploratory data analysis on large datasets using Python, pyspark, SQL, and visualization libraries.
Design, implement, and validate machine learning and statistical models to address complex healthcare and insurance challenges.
Collaborate with DevOps engineers to productionize models using containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines.
Build and maintain reusable ML accelerators that standardize feature engineering, model training, and evaluation across tasks.
Facilitate technical workshops and presentations to ensure clarity and buy-in across diverse audiences.
Advocate for responsible AI by incorporating fairness, explainability, and bias detection into model development.

Bachelor’s/ master’s degree in data science, Statistics, Applied Mathematics, Computer Science, or a related field and around 8 to 10 years of industry experience
Highly Preferred: PhD in a relevant quantitative field.
Advanced certifications in Microsoft Azure and modern data/ML platform highly preferred.
Strong proficiency in Python/ Pyspark (data wrangling, EDA, modeling) and SQL for working with large, complex datasets; advanced Excel for analysis and validation.
Experience in defining evaluation taxonomies and acceptance criteria across initiatives; balances statistical and operational risk.
Experience in codifing analytical playbooks and institutionalizes measurement frameworks across products/teams.
Proven experience in balancing arbitrates trade-offs (accuracy, fairness, latency, interpretability) for high impact decisions.
Proven track record of putting model into production and monitoring.
Experience with Azure Databricks, Data bricks, for scalable data processing, model training, and orchestration.
Knowledge of data privacy/security best practices across workflows.
Knowledge of applying Responsible AI principles into model building, comprehensive documentation and audit trails for compliance experience.
Experience in running multiple projects and conducting/overseeing high stakes experiments and peer reviews for critical models.

Senior Data Scientist

Key skills